Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over SEMOV PLAMEN ET AL: "Adaptive Resource Scheduling based on Neural Network and Mobile Traffic Prediction", 2019 42ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), IEEE, 1 July 2019 (2019-07-01), pages 585-588 (from IDS filed by Applicant 8/15/25) in view of Laird-McConnell US 11349679
1. A method, performed by an electronic device communicating with one or more user equipments (UEs), the method comprising:
identifying one or more measurement indicators for a first UE communicating with the electronic device in a first time section (SEMOV PLAMEN - p. 586, left column, lines10-19 (i.e. second para), time intervals; p. 586 left column lines 39-50: buffer status is used for determining scheduling algorithm for next time interval);
generating a first heatmap for the first time section, based on the one or more measurement indicators for the first UE (SEMOV PLAMEN - p. 586, left column line 51 - right column line -11), wherein a dimension of the first heatmap corresponds to a number of the one or more measurement indicators (SEMOV PLAMEN – fig. 2 – e.g., User Heatmap ‘UHM’ dimension are time in millisecond…” p. 586, section II & III);
calculating one or more scheduling parameters based on the first heatmap by using at least one neural network model (SEMOV PLAMEN - p. 587, neural network architecture; p. 586 section II & Ill related text – e.g., the BHM as a final heatmap is being used to training the AI agent and used as information for traffic and decision making); and
determining, based on the one or more scheduling parameters, scheduling priorities for the one or more UEs (SEMOV PLAMEN - p. 585 left column, lines 26-42; right column lines 31-44: scheduling algorithms based on prioritization are used in conjunction with the disclosed traffic state prediction using heatmaps, as illustrated above; p. 586 section II & Ill related text – e.g., the UHM information is stored in the buffer together with additional buffer parameters such as packet priority; the time a packet remained in the buffer; the data left from a packet for transmission. Based on them the BHM is created).
However, SEMOV PLAMEN merely discloses the term a processor & a memory
Laird-McConnell further teaches (Laird-McConnell: fig. 5, unit 502 & 504) in order to determine an amount of communication received from each of the plurality of client systems for each of the plurality of agenda items; generate a heatmap representing the amount of communication received from each of the plurality of client systems for each of the plurality of agenda items; and cause the heatmap to be displayed at one or more of the client systems (col. 3, lines 27-40).
Thus, it would have been obvious to one skill in the art before the effective filing date of the claim invention to include the above recited limitation into SEMOV PLAMEN’s invention in order to determine an amount of communication received from each of the plurality of client systems for each of the plurality of agenda items; generate a heatmap representing the amount of communication received from each of the plurality of client systems for each of the plurality of agenda items; and cause the heatmap to be displayed at one or more of the client systems (col. 3, lines 27-40), as taught by Laird-McConnell.
2. The method of claim 1, further comprising: identifying one or more measurement indicators for a second UE communicating with the electronic device in a second time section; and generating a second heatmap for the second time section, based on the one or more measurement indicators for the second UE, wherein the calculating of the one or more scheduling parameters comprises calculating, by using the at least one neural network model, the one or more scheduling parameters based on a plurality of heatmaps respectively generated for a plurality of time sections, wherein the plurality of time sections comprises the first time section and the second time section, and wherein the plurality of heatmaps comprises the first heatmap and the second heat map (SEMOV PLAMEN - fig. 2, plurality of heatmaps, also p. 586 left column line 51 - right column line -11: heat maps are prepared over time and per user).
3. The method of claim 2, wherein the at least one neural network model comprises a first sub-model and a second sub-model, and wherein the calculating of the one or more scheduling parameters based on the plurality of heatmaps respectively generated for the plurality of time sections comprises: extracting a plurality of features from the plurality of heatmaps by using the first sub-model; deriving a plurality of time-dependent features based on the plurality of features by using the second sub-model; and calculating the one or more scheduling parameters, based on the plurality of time-dependent features (Laird-McConnell: fig. 2-3, col. 7, lines 55-col. 8, lines 16, col. 9, lines 60-col. 11, lines 40).
4. The method of claim 2, wherein the plurality of time sections comprise sequential time sections, and wherein a number of the plurality of heatmaps is determined based on at least one of an environmental factor or a configuration value associated with the electronic device (SEMOV PLAMEN - fig. 2, plurality of heatmaps, also p. 586 left column line 51 - right column line -11: heat maps are prepared over time and per user).
5. The method of claim 1, wherein a length of the first time section is determined based on the at least one of an environmental factor or a configuration value associated with the electronic device (SEMOV PLAMEN - fig. 2, plurality of heatmaps, also p. 586 left column line 51 - right column line -11: heat maps are prepared over time and per user).
6. The method of claim 1, wherein a size of the first heatmap is determined based on a minimum value and a maximum value of each of the one or more measurement indicators, and wherein at least one of the minimum value or the maximum value of each of the one or more measurement indicators is determined based on a configuration value associated with the electronic device (SEMOV PLAMEN – fig. 2, section 1 & section II, Tale 1 – e.g., in fig. 2 the UHM dimensions are time in milliseconds……).
7. The method of claim 1, wherein the one or more measurement indicators comprise at least one of buffer occupancy, an average throughput, or a modulation and coding scheme (MCS) index, and wherein the one or more scheduling parameters comprise one or more variables for a generalized proportional fairness (GPF) metric (SEMOV PLAMEN - p. 586 left column lines 39-50: buffer status is used for determining scheduling algorithm for next time interval).
8. The method of claim 1, wherein the at least one neural network model is trained via a reinforcement learning and infers the one or more scheduling parameters (SEMOV PLAMEN - abstract, NN is used throughout).
9. The method of claim 8, wherein a state variable for the reinforcement learning comprise the one or more measurement indicators, wherein an action variable for the reinforcement learning comprises the one or more scheduling parameters, and wherein a reward variable for the reinforcement learning comprises an user perceived throughput and a packet delay violation (SEMOV PLAMEN - fig. 2 6-7, p. 585, left column - a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic.).
10. The method of claim 1, wherein the one or more UEs are active UEs communicating with the electronic device and resources are assigned to the one or more UEs, based on the scheduling priorities of the one or more UEs (SEMOV PLAMEN - section II, scheduling).
Regarding claims 9-20, the independent claim and each dependent claim are related to the same limitation set for hereinabove in claims 1-10, where the difference used is a “device & CRM” with a processor and a memory (Laird-McConnell: fig. 5, unit 502 & 504) and the wordings of the claims were interchanged within the claim itself or some of the claims were presented as a combination of two or more previously presented limitations. This change does not affect the limitation of the above treated claims. Adding these phrases to the claims arid interchanging the wording did not introduce new limitations to these claims. Therefore, these claims were rejected for similar reasons as stated above.
Response to Arguments
Applicant's arguments filed on 12/17/25 have been fully considered but they are not persuasive.
Applicant Argument:
According to amended claim 1, calculating one or more scheduling parameters based on the first heatmap by using at least one neural network model; and determining, based on the scheduling parameters, scheduling priorities for the one or more UEs. Such a distinction is not disclosed in prior art(s).
Response to Arguments:
With respect to the above argument, Examiner would like to draw attention to that it is the claims that define the claimed invention, and it is claims, not specifications that are anticipated or unpatentable. Constant v. Advanced Micro-Devices Inc., 7 USPQ2d 1064.
In addition, the Examiner would like to draw attention to (p. 587, neural network architecture; p. 586 section II & Ill related text) of SEMOV PLAMEN, for example: the BHM as a final heatmap is being used to training the AI agent and used as information for traffic and decision making (herein it’s considered same as calculating one or more scheduling parameters based on the first heatmap by using at least one neural network model); and
In addition, the Examiner would like to draw attention to (fig. 1-2, p. 585 left column, lines 26-42; right column lines 31-44; p. 586 section II & Ill related text) of SEMOV PLAMEN, for example: scheduling algorithms based on prioritization are used in conjunction with the disclosed traffic state prediction using heatmaps, as illustrated above; further in p. 586 section II & Ill related text – e.g., the UHM information is stored in the buffer together with additional buffer parameters such as packet priority; the time a packet remained in the buffer; the data left from a packet for transmission. Based on them the BHM is created (i.e., the BHM is determined) (herein it’s considered same as determining, based on the one or more scheduling parameters, scheduling priorities for the one or more UEs).
Thus, for the above reason, the prior art meets the claim limitation.
The examiner stresses that the claims are too broad and require detail or specialization of the steps as recited in the claims. Alone and as claimed, the limitations are too open.
Examiner has cited particular portions of the references as applied to each claim limitation for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Regarding all other arguments presented by applicant, the arguments are substantially the same as those which have already been addressed above and in the interest of brevity; the Examiner directs the applicant to those responses above.
Remark:
In addition, an interview could expedite the prosecution.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sulaiman Nooristany whose telephone number is 571-270-1929. The examiner can normally be reached on Monday thru Friday: 8:30am to 5:00pm (EST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeff Rutkowski can be reached on 571-270-1215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SULAIMAN NOORISTANY/
Primary Examiner, Art Unit 2415